Mental health constitutes a fundamental aspect of overall health and is recognized as a basic human right [1]. According to a report from the World Health Organization (WHO), an estimated 970 million people worldwide have mental disorders, such as depression, anxiety, and eating disorders, creating a substantial demand for mental health services [2]. However, nearly half of the global population resided in countries where there was only 1 psychiatrist available for every 200,000 individuals or more [2]. The severe imbalance between the supply and demand for mental health professionals poses a significant challenge to global public health.

The development of Web 2.0 has facilitated the emergence of online health communities (OHCs), greatly bridging the accessibility gap in mental health services [3]. These platforms serve as a novel channel for individuals to access information and emotional support [4], aligning with the delivery of mental health services. First, compared to physical diseases, mental health issues are less tangible and can often be mitigated through psychological counseling [5,6]. Online counseling, a central component of OHCs, uses video, voice, and text to overcome geographical and temporal barriers [7], thereby enhancing the accessibility of mental health services. Second, the societal stigma associated with mental health issues may cause individuals to avoid professional health services or hesitate to seek help [8]. Online counseling presents an advantage in this context by ensuring anonymity, enabling individuals to undergo counseling privately without the fear of recognition by acquaintances.

OHCs for mental health have been increasingly acknowledged and used, especially in emerging economies like China, where medical resources are limited but internet access is prevalent [9]. In recent years, driven by a significant increase in demand, OHCs for psychological counseling in China have experienced rapid development. According to the China Internet Network Information Center, by the end of 2023, the number of internet users in China had exceeded 1 billion, providing a solid foundation for the promotion of OHC services [10]. Moreover, the cultural background and social structure in China differ from those in Western countries, which may influence the acceptance and implementation of online psychological counseling [11]. For example, mental health issues carry a significant stigma in Chinese society, making the anonymity of online counseling more appealing [12]. In addition, mental health resources in China are unevenly distributed, particularly in rural and remote areas where these resources are scarce [13]. The advancement of internet technology has enabled OHCs to provide convenient mental health services to residents in these areas, making them particularly relevant in the Chinese context [14]. Thus, choosing China as the focus of this study not only helps to reveal unique phenomena in the field of OHCs in China but also provides experience and references for the global development of online psychological counseling.

Unlike offline services, online services frequently occur between parties lacking any prior transaction history [15]. This absence of prior interaction usually leaves consumers without the opportunity to evaluate or trial the service, thereby compelling them to assume the “risk of prior performance” [16]. In the health care sector, information asymmetry is more marked: whereas health professionals understand the characteristics of their services, consumers possess limited insight [17]. Therefore, the decision-making processes of individuals in selecting among numerous online psychological counselors have become a critical issue. Signaling theory provides a theoretical framework for understanding this information asymmetry and suggests potential solutions [18]. According to signaling theory, information asymmetry can be mitigated when the parties with more information share some signals with the other parties, and the recipient of these signals can interpret them and adjust their behavior accordingly [19]. Within the environment of OHCs, health care providers disclose information to potential users, such as price and reputation, and people can use this information to make what they perceive to be the appropriate choice for their situations [20].

Existing research has demonstrated the positive impact of online psychological counseling. For example, studies have shown that online counseling can significantly improve clients’ emotional and behavioral issues [7,21-23]. However, little research addresses the decision-making process clients undergo when selecting online counselors. At the same time, although previous studies have demonstrated that online information in OHCs can influence consumer choices, few have applied signaling theory to online psychological counseling. In addition, given that OHCs cover a wide range of diseases, the needs and preferences of signal receivers may vary [20]. Therefore, the research question for this study is as follows: What factors influence clients’ choice of counselors in OHCs? To answer this question, this paper constructs a theoretical model based on signaling theory. Furthermore, we chose to study both online and offline signals to systematically identify the factors that influence clients’ choices of online psychological counselors.

We selected YiDianLing as our data sampling community for several reasons. First, YiDianLing is one of the largest OHCs in China dedicated to mental health, with approximately 39 million registered users and 42,000 resident professional psychological counselors. This extensive user base ensures a diverse and comprehensive dataset, which increases the generalizability of the results. Second, the platform’s services are available nationwide, providing a broad geographic representation of clients and counselors from different regions across China. This geographic diversity further enhances the generalizability of our study’s findings within the context of the Chinese mental health landscape. Third, YiDianLing offers both chargeable services and free consultations, providing data to support our research on the prosocial behavior of psychological counselors. Fourth, YiDianLing maintains high certification standards for mental health service providers. All applicants must submit relevant offline information, and only those who meet the platform’s certification criteria can establish their home pages. This ensures the reliability and professionalism of the counselors included in the study. Fifth, psychological counselors registered on YiDianLing have personal home pages that contain quantitative data relevant to this study, such as the total number of consultations, service price, and free consultations. Figures 2 and 3 provide detailed views of a counselor’s home page. Therefore, YiDianLing provides a robust and reliable data source that aligns well with the objectives of our research.

We developed a web crawler program to collect information from the personal home pages of psychological counselors on YiDianLing. Although there are many registered counselors on YiDianLing, the platform only showcases approximately the top 1000 active counselors. To ensure the representativeness of our study subjects, we used a 3-step selection process. First, the counselors must have provided services to at least 1 client on the platform. Second, these counselors must have available services for booking within the next 5 days to confirm their active status on the platform. Finally, their information on the home pages had to be complete and include all the target data for our research. Based on these criteria, we initially identified 667 counselors on the YiDianLing platform as our research sample.

To further ensure the validity of the sample data, we collected profile information of the same counselors at 4 different time points (March, June, September, and December 2023). This phased data collection method allowed us to account for potential time-related effects. It enabled us to capture a more comprehensive picture of user behavior and service demand dynamics, avoiding the randomness and partiality associated with data from a single time point.

Following data collection, we conducted comprehensive data cleaning and preprocessing to eliminate missing and duplicate data. Ultimately, we reduced the initial sample of 667 counselors to 487 psychological counselors as the final sample. We used this final sample to create a balanced 4-period panel dataset.

The data for this study were sourced from publicly accessible information available to all users of the online platform. We did not collect any personal information about the clients and anonymized data about counselors. The study was approved by the Ethics Committee of Tongji Medical College of Huazhong University of Science & Technology (IORG0003571).

Table 2 presents the correlation coefficients of all variables for the analysis. The results show that almost all of the independent variables exhibit a correlation with the dependent variable. The correlations among the independent variables are significantly low. Moreover, the variance inflation factor values of the independent and control variables are less than 2, indicating that multicollinearity is not a concern in our model.

Table 3 presents the results of our empirical models. Throughout our analysis, we systematically incorporated variables, culminating in the construction of 4 distinct models. We observed a consistent increase in R² from 0.337 in Model 1 to 0.962 in Model 4 with the inclusion of variables. The significance levels of the key variables remained consistent across all models. Therefore, we chose to interpret our research results using the final model.

As shown in Model 4, in the online signals category, both price (β₁=.186, P＜.001) and online reputation (β₄=.489, P=.002) positively affect the clients’ choices, thereby supporting H1 and H3. Based on our previous assumptions, we incorporated prosocial behavior and its quadratic term into the empirical model. We find a significant inverted U-shaped relationship between prosocial behavior and the clients’ choices (β₂=.190, P=.002; β₃=–.018, P=.001). After conducting the fixed effects model analysis, we used the U-shape test estimation algorithm by Lind and Mehlum [66] to verify the stability of the inverted U-shaped relationship. The procedure involved 4 main steps. First, we used the Wald test to assess the significance of the linear and nonlinear combinations of prosocial behaviors. The P value of the Wald test was .004, indicating that these variables significantly contributed to the model. Second, we calculated the slope of the curve at the low and high points of prosocial behavior. According to our research data, the low point was 2.40 and the high point was 6.90. The results showed that the slope of prosocial behavior at the low point was 0.102, and at the high point, it was –0.060. Third, we used the likelihood ratio test to verify whether the influence of prosocial behavior on clients’ choices increased monotonically at low levels of prosocial behavior and decreased monotonically at high levels. The P value of the likelihood ratio test was .001, indicating that this nonlinear relationship was significant. Fourth, we used the Delta confidence interval test to verify whether the poles of the curve fell within the range of high and low values of prosocial behaviors. The results showed that the poles fell within the interval between the 2 values, indicating that the inverted U-shape was considered stable. Therefore, H2 is supported. In the offline signals category, we discovered that practical experience (β₆=.911, P＜.001) has a positive impact on clients’ choices, supporting H4b. However, the effect of professional titles and educational levels on the clients’ choices was found to be insignificant; thus, H4a and H4c are not supported.

In the robustness check section, we divided the dataset into 2 groups based on service duration. These two subsamples included (1) psychological counselors with fewer than 1000 hours of service and (2) those with 1000 hours or more. As service duration is consistent within each group, we did not include this factor in the model. Table 4 shows that the findings from these subsamples correspond with the primary results from the full dataset. Therefore, research findings exhibit high robustness and reliably support our preliminary results.

In this paper, we draw on signaling theory to investigate how information on psychological counselors’ home pages influences clients’ choices. The findings highlight the crucial role of online and offline signals in shaping decision-making processes and demonstrate that effective information disclosure can encourage consumers to select a psychological counselor.

In the context of online signals, it has been found that both price and online reputation affect a client’s choice of psychological counselor, aligning with the findings of previous relevant studies [26,42,43,67-69]. On the one hand, given that consumers might not possess the requisite expertise to evaluate professional services, clients are often willing to pay a premium in exchange for the assurance of higher service quality [39]. As prices increase, consumers expect better services from a counselor, and thus are more inclined to select that counselor. On the other hand, a strong online reputation can boost consumers’ trust in a counselor’s professional skills. Demonstrating an online reputation highlights the consistency and professionalism of a counselor’s service quality and reveals the professional attitude and commitment they maintain during service delivery.

In the context of offline signals, it was determined that practical experience has a positive impact on a client’s choice of a psychological counselor. Offline signals mainly offer insights into a counselor’s personal attributes, thus often considered reflective of their skills and professional standards. Although offline signals originate from a physical environment, this study demonstrates that consumers attribute significant value to them and recognize their essential role in decision making. In other words, even in the digital sphere, the importance and impact of offline signals are preserved.

Interestingly, we discovered an inverted U-shaped relationship between a psychological counselor’s prosocial behavior and clients’ choices. This suggests that while clients appreciate a certain level of prosocial behavior, an overly excessive display may arouse suspicion and negatively affect their choices. Specifically, moderate prosocial behavior can improve a counselor’s image and online reputation, thus enhancing client trust. However, exceeding a certain threshold of prosocial behavior may be perceived as insincere, resulting in increased client skepticism toward the counselor.

Our study broadens the scope of theoretical research on signaling theory. First, we introduce signaling theory into the specific service domain of online psychological counseling and establish a theoretical framework to clarify the role of online and offline signals in the process of clients choosing a psychological counselor. The results of this study align with Zhou et al [70], which highlights the significance of both demonstration and description signals in online mental health markets and demonstrates how various types of signals can influence clients’ choices.

Second, this study considers the service price, online reputation, and prosocial behavior as signals and incorporates them into the research framework, which is an approach previously unexplored in previous studies. Previous studies have often focused on isolated online signals, such as online reviews and ratings [25,71,72]. Our research integrates both online signals and offline signals, offering a more comprehensive framework that enhances the understanding of how different signals interact to influence clients’ choices.

Finally, our research results reveal an inverted U-shaped relationship between a psychological counselor’s prosocial behavior and client’s choices. This finding addresses a gap in the literature, where prosocial behavior was generally considered to have a linear positive effect [73-76]. Our study shows that there is an optimal level of prosocial behavior, beyond which client skepticism may arise, thus contributing to a more nuanced understanding of the dynamics at play.

For psychological counselors in OHCs, our study reveals the positive influence of service price on clients’ choices. Consequently, psychological counselors should leverage their pricing autonomy in OHCs, using their service price as a signal of their capability to convey accurate quality signals to potential clients. Moreover, considering the significant impact of an online reputation on client’s choice, it is crucial for psychological counselors to carefully manage client relationships and maintain their online reputation. This suggests that, besides providing high-quality services, psychological counselors should proactively respond to client feedback, engage in OHC activities, and enhance their online visibility to gain a competitive edge in the domain of online psychological counseling [77]. Finally, our research exposes an inverted U-shaped relationship between a psychological counselor’s prosocial behavior and clients’ choices. This finding suggests that psychological counselors must adeptly balance and temper their prosocial behavior in practice, aiming for an ideal equilibrium that attracts clients while maintaining a professional image.

For OHC platforms, despite operating in a digital environment, offline signals from psychological counselors still play a significant role in client selection. Therefore, platforms may consider displaying more offline personal information on the counselors’ profile pages, such as their affiliated institutions and accolades, which can enhance information transparency and boost client trust. At the same time, platforms need to recognize the high level of user attention given to key signals such as price, online reputation, and prosocial behavior. As a result, when designing web and app interfaces, platforms should strategically position this information prominently, allowing users to quickly and efficiently access these details.

We admit some limitations in our study. First, this study uses data from a single platform, YiDianLing, collected at 4 intervals throughout the year. While YiDianLing is a reliable source, relying on one website may limit the generalizability of our findings. The 4 data collection periods were chosen to cover key times of the year, providing a representative snapshot. However, future research should incorporate data from multiple platforms and extend the observation period to enhance the findings’ robustness and applicability. Second, although our study on online psychological counseling in China offers valuable insights, there is significant variability in counseling services across different countries, particularly in terms of providers and content. Future research should include data from multiple countries to capture a more comprehensive view of global trends and differences in online psychological counseling. Third, this study focuses on using signaling theory to explore clients’ decision-making processes when choosing online psychological counselors, emphasizing the behaviors prior to engaging in online counseling. However, psychological counseling is inherently a long-term and complex process. Future research should incorporate assessments of the long-term outcomes and effectiveness of counseling sessions to comprehensively evaluate the overall process of online psychological counseling. Additionally, the results and effectiveness of clients’ previous psychological counseling sessions may also influence their choices to some extent. Due to privacy protections, YiDianLing has not disclosed data regarding these past experiences. Future research should consider addressing this limitation by finding ways to incorporate such data while ensuring privacy protection. Fourth, while we considered regional diversity in our sample selection and controlled for the city tiers of psychological counselors in our data analysis, the lack of direct data on clients’ locations may impact the generalizability of our results. Under the condition of complying with relevant regulations, future research should collect and analyze data on clients’ locations to enhance the study’s comprehensiveness. Fifth, counselors’ home pages also contain some textual data that disclose clients’ experiences, but this paper only analyzed some quantitative data and future research can do deeper mining of textual data of OHCs.

This study uses signaling theory to investigate client preferences for psychological counselors. We segmented the information displayed on counselors’ home pages into online and offline signals and established a theoretical model for client decision making using data derived from a Chinese online health community. The findings reveal that service price, online reputation, professional titles, and practical experience positively influence clients’ choices. Additionally, we discovered an inverted U-shaped correlation between a psychological counselor’s prosocial behavior and clients’ choices. Fundamentally, this study unravels a complex client selection process influenced by different signals, providing valuable guidance for both researchers and practitioners.